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Open AccessArticle

A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species

1
Friedrich-Loeffler-Institut, Institute of Bacterial Infections and Zoonoses, Naumburger Str. 96a, 07743 Jena, Germany
2
Animal Health Research Institute, Agricultural Research Center, 12618 Dokki-Giza, Egypt
3
Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University, Helmholtzweg 4, 07743 Jena, Germany
4
InfectoGnostics Research Campus Jena, Center of Applied Research, Philosophenweg 7, 07743 Jena, Germany
5
Leibniz-Institute of Photonic Technology, Member of the Leibniz Research Alliance – Leibniz Health Technologies, Albert-Einstein-Str. 9, 07745 Jena, Germany
6
Institute for Animal Hygiene and Environmental Health, Free University Berlin, Robert-von Ostertag-Str. 7–13, 14163 Berlin, Germany
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Dmitry Kurouski
Molecules 2019, 24(24), 4516; https://doi.org/10.3390/molecules24244516
Received: 5 November 2019 / Revised: 3 December 2019 / Accepted: 4 December 2019 / Published: 10 December 2019
(This article belongs to the Section Analytical Chemistry)
Burkholderia (B.) mallei, the causative agent of glanders, and B. pseudomallei, the causative agent of melioidosis in humans and animals, are genetically closely related. The high infectious potential of both organisms, their serological cross-reactivity, and similar clinical symptoms in human and animals make the differentiation from each other and other Burkholderia species challenging. The increased resistance against many antibiotics implies the need for fast and robust identification methods. The use of Raman microspectroscopy in microbial diagnostic has the potential for rapid and reliable identification. Single bacterial cells are directly probed and a broad range of phenotypic information is recorded, which is subsequently analyzed by machine learning methods. Burkholderia were handled under biosafety level 1 (BSL 1) conditions after heat inactivation. The clusters of the spectral phenotypes and the diagnostic relevance of the Burkholderia spp. were considered for an advanced hierarchical machine learning approach. The strain panel for training involved 12 B. mallei, 13 B. pseudomallei and 11 other Burkholderia spp. type strains. The combination of top- and sub-level classifier identified the mallei-complex with high sensitivities (>95%). The reliable identification of unknown B. mallei and B. pseudomallei strains highlighted the robustness of the machine learning-based Raman spectroscopic assay. View Full-Text
Keywords: Glanders; melioidosis; Raman spectroscopy; SVM; PCA; Burkholderia mallei; Burkholderia pseudomallei; heat inactivation Glanders; melioidosis; Raman spectroscopy; SVM; PCA; Burkholderia mallei; Burkholderia pseudomallei; heat inactivation
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MDPI and ACS Style

Moawad, A.A.; Silge, A.; Bocklitz, T.; Fischer, K.; Rösch, P.; Roesler, U.; Elschner, M.C.; Popp, J.; Neubauer, H. A Machine Learning-Based Raman Spectroscopic Assay for the Identification of Burkholderia mallei and Related Species. Molecules 2019, 24, 4516.

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